| Title | Source Code Vulnerability Mining Method based on Graph Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Jiang, Zhenghong |
| Conference Name | 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI) |
| Keywords | codes, coding theory, composability, compositionality, Correlation, cryptography, feature extraction, Graph Neural Network, Manuals, Metrics, Mining Method, Optimization methods, pubcrawl, resilience, Resiliency, security, source code vulnerability, Training, Uncertainty |
| Abstract | Vulnerability discovery is an important field of computer security research and development today. Because most of the current vulnerability discovery methods require large-scale manual auditing, and the code parsing process is cumbersome and time-consuming, the vulnerability discovery effect is reduced. Therefore, for the uncertainty of vulnerability discovery itself, it is the most basic tool design principle that auxiliary security analysts cannot completely replace them. The purpose of this paper is to study the source code vulnerability discovery method based on graph neural network. This paper analyzes the three processes of data preparation, source code vulnerability mining and security assurance of the source code vulnerability mining method, and also analyzes the suspiciousness and particularity of the experimental results. The empirical analysis results show that the types of traditional source code vulnerability mining methods become more concise and convenient after using graph neural network technology, and we conducted a survey and found that more than 82% of people felt that the design source code vulnerability mining method used When it comes to graph neural networks, it is found that the design efficiency has become higher. |
| DOI | 10.1109/ICETCI55101.2022.9832392 |
| Citation Key | jiang_source_2022 |
Source Code Vulnerability Mining Method based on Graph Neural Network